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Items: 1 to 20 of 88

1.

Microarrays: how many do you need?

Zien A, Fluck J, Zimmer R, Lengauer T.

J Comput Biol. 2003;10(3-4):653-67.

PMID:
12935350
2.

Comparison of seven methods for producing Affymetrix expression scores based on False Discovery Rates in disease profiling data.

Shedden K, Chen W, Kuick R, Ghosh D, Macdonald J, Cho KR, Giordano TJ, Gruber SB, Fearon ER, Taylor JM, Hanash S.

BMC Bioinformatics. 2005 Feb 10;6:26.

3.

Ranking analysis for identifying differentially expressed genes.

Qi Y, Sun H, Sun Q, Pan L.

Genomics. 2011 May;97(5):326-9. doi: 10.1016/j.ygeno.2011.03.002. Epub 2011 Mar 22.

4.

Use of normalization methods for analysis of microarrays containing a high degree of gene effects.

Ni TT, Lemon WJ, Shyr Y, Zhong TP.

BMC Bioinformatics. 2008 Nov 28;9:505. doi: 10.1186/1471-2105-9-505.

5.

A statistical framework for the design of microarray experiments and effective detection of differential gene expression.

Zhang SD, Gant TW.

Bioinformatics. 2004 Nov 1;20(16):2821-8. Epub 2004 Jun 4.

PMID:
15180939
6.

Sources of variability and effect of experimental approach on expression profiling data interpretation.

Bakay M, Chen YW, Borup R, Zhao P, Nagaraju K, Hoffman EP.

BMC Bioinformatics. 2002;3:4. Epub 2002 Jan 31.

7.

Assessing statistical significance in microarray experiments using the distance between microarrays.

Hayden D, Lazar P, Schoenfeld D; Inflammation and the Host Response to Injury Investigators.

PLoS One. 2009 Jun 16;4(6):e5838. doi: 10.1371/journal.pone.0005838.

8.

Considerations when using the significance analysis of microarrays (SAM) algorithm.

Larsson O, Wahlestedt C, Timmons JA.

BMC Bioinformatics. 2005 May 29;6:129.

9.
10.

A renewed approach to the nonparametric analysis of replicated microarray experiments.

Jung K, Quast K, Gannoun A, Urfer W.

Biom J. 2006 Apr;48(2):245-54.

PMID:
16708776
11.

Microarray data analysis: from disarray to consolidation and consensus.

Allison DB, Cui X, Page GP, Sabripour M.

Nat Rev Genet. 2006 Jan;7(1):55-65. Review. Erratum in: Nat Rev Genet. 2006 May;7(5):406.

PMID:
16369572
12.

The PowerAtlas: a power and sample size atlas for microarray experimental design and research.

Page GP, Edwards JW, Gadbury GL, Yelisetti P, Wang J, Trivedi P, Allison DB.

BMC Bioinformatics. 2006 Feb 22;7:84.

13.

A Population Proportion approach for ranking differentially expressed genes.

Gadgil M.

BMC Bioinformatics. 2008 Sep 18;9:380. doi: 10.1186/1471-2105-9-380.

14.

A non-transformation method for identifying differentially expressed genes from cDNA microarrays.

Zhang JG, Yin ZJ, Zhang Q.

Yi Chuan Xue Bao. 2006 Jan;33(1):80-8.

PMID:
16450591
15.

The effects of normalization on the correlation structure of microarray data.

Qiu X, Brooks AI, Klebanov L, Yakovlev N.

BMC Bioinformatics. 2005 May 16;6:120.

16.

Bias in error estimation when using cross-validation for model selection.

Varma S, Simon R.

BMC Bioinformatics. 2006 Feb 23;7:91.

17.

Can Zipf's law be adapted to normalize microarrays?

Lu T, Costello CM, Croucher PJ, Häsler R, Deuschl G, Schreiber S.

BMC Bioinformatics. 2005 Feb 23;6:37.

18.

From patterns to pathways: gene expression data analysis comes of age.

Slonim DK.

Nat Genet. 2002 Dec;32 Suppl:502-8. Review.

PMID:
12454645
19.

Sample size calculations based on ranking and selection in microarray experiments.

Matsui S, Zeng S, Yamanaka T, Shaughnessy J.

Biometrics. 2008 Mar;64(1):217-26. Epub 2007 Aug 3.

PMID:
17680829
20.

Microarray data quality control improves the detection of differentially expressed genes.

Kauffmann A, Huber W.

Genomics. 2010 Mar;95(3):138-42. doi: 10.1016/j.ygeno.2010.01.003. Epub 2010 Jan 14. Review.

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